Update app.py

app.py

@@ -2,34 +2,34 @@ import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
-
-# Load your custom regression model
-model_path = "Ditto-premiumdelux-model_transferlearning.weights.h5"
-model_path = "Ditto-premiumdelux-model_transferlearning.keras"
-
-#model.load_weights(model_path)
-model = tf.keras.models.load_model(model_path)
-
+ 
 labels = ['Ditto','Golbat','Koffing']
-
-# Define regression function
-def predict_regression(image):
-    # Preprocess image
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
-    image = np.array(image)
-    print(image.shape)
-    # Predict
-    prediction = model.predict(image[None, ...])  # Assuming single regression value
-    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
-    return confidences
-
-# Create Gradio interface
-input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Pokemon")
-interface = gr.Interface(fn=predict_regression, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         examples=["images/Ditto.jpeg", "images/Golbat.jpeg", "images/Koffing.jpeg"],   
-                         description="A simple mlp classification model for image classification using the mnist dataset.")
-interface.launch()
+ 
+def predict_pokemon_type(uploaded_file):
+    
+    if uploaded_file is None:
+        return "No file uploaded."
+   
+    model = tf.keras.models.load_model('Ditto-premiumdelux-model_transferlearning.keras')
+    # Load the image from the file path
+    with Image.open(uploaded_file) as img:
+        img = img.resize((150, 150)).convert('RGB')  # Convert image to RGB
+        img_array = np.array(img)
+ 
+        prediction = model.predict(np.expand_dims(img_array, axis=0))
+        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+ 
+        return confidences
+ 
+ 
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_pokemon_type,  # Function to process the input
+    inputs=gr.File(label="Upload File"),  # File upload widget
+    outputs="text",  # Output type
+    title="Pokemon Classifier",  # Title of the interface
+    description="Upload a picture of a pokemon (preferably Ditto, Golbat, Koffing)"  # Description of the interface
+)
+ 
+# Launch the interface
+iface.launch()